Diego S. Azevedo, L. F. Costa, A. Brito, T. Nascimento
{"title":"Analysis of Prediction Models for Multi-robot System NMPFC","authors":"Diego S. Azevedo, L. F. Costa, A. Brito, T. Nascimento","doi":"10.1109/SBR.LARS.ROBOCONTROL.2014.12","DOIUrl":null,"url":null,"abstract":"This paper analyzes the prediction model of a nonlinear model predictive formation controller (NMPFC) applied to control the formation of a team of omni directional mobile robots. The prediction model calculates future formation behaviors with respect to obstacles, team mates in formation, target, orientation, position in formation and control effort using a kinematic model to predict most of the formation terms. Nevertheless, the prediction of the robots in formation can be done by either dynamic models or pure kinematic models. Therefore, this paper presents an analysis in order to find a balanced robot's prediction model that stimulates the robots the converge in less time and minimizing the controller's cost function. Finally, results of experiments with simulated robots are presented and discussed.","PeriodicalId":264928,"journal":{"name":"2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SBR.LARS.ROBOCONTROL.2014.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This paper analyzes the prediction model of a nonlinear model predictive formation controller (NMPFC) applied to control the formation of a team of omni directional mobile robots. The prediction model calculates future formation behaviors with respect to obstacles, team mates in formation, target, orientation, position in formation and control effort using a kinematic model to predict most of the formation terms. Nevertheless, the prediction of the robots in formation can be done by either dynamic models or pure kinematic models. Therefore, this paper presents an analysis in order to find a balanced robot's prediction model that stimulates the robots the converge in less time and minimizing the controller's cost function. Finally, results of experiments with simulated robots are presented and discussed.